Class Imbalance in Software Fault Prediction Data Set

Classification has been the prominent technique in machine learning domain, due to its ability of forecasting and predicts capabilities it is widely used in various domains such as health care, networking, social network, and software engineering with enhancement of different algorithm. The performance of the classifier majorly depends on the quality and amount of data present in the training sample. In real-world scenario, the majority of training samples suffered from class imbalance problem, that is, most of the data samples belong to one particular category, i.e., majority class while very few represent the minority class. In this case, classification techniques tend to be overwhelmed by the majority class and ignore the minority class. To solve class imbalance problem people relay on the different kind of sampling techniques either by generating synthetic data or by concentrating on minority class samples, but those approaches have introduced adverse effect in the learnability. In this paper, we attempt to study different techniques proposed to solve the class imbalance problem.